135,431 research outputs found
Attribute-Guided Face Generation Using Conditional CycleGAN
We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method.Comment: ECCV 201
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
The objective of this paper is 3D shape understanding from single and
multiple images. To this end, we introduce a new deep-learning architecture and
loss function, SilNet, that can handle multiple views in an order-agnostic
manner. The architecture is fully convolutional, and for training we use a
proxy task of silhouette prediction, rather than directly learning a mapping
from 2D images to 3D shape as has been the target in most recent work.
We demonstrate that with the SilNet architecture there is generalisation over
the number of views -- for example, SilNet trained on 2 views can be used with
3 or 4 views at test-time; and performance improves with more views.
We introduce two new synthetics datasets: a blobby object dataset useful for
pre-training, and a challenging and realistic sculpture dataset; and
demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally,
we show that SilNet exceeds the state of the art on the ShapeNet benchmark
dataset, and use SilNet to generate novel views of the sculpture dataset.Comment: BMVC 2017; Best Poste
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